快速和可扩展的脑纤维束图最佳传输
Fast and Scalable Optimal Transport for Brain Tractograms
摘要：
我们提出了一种新的多尺度算法，用于在GPU上解决正则化最优运输问题，具有线性内存占用。依靠Sinkhorn分歧，这是凸的，光滑的和正定的损失函数，这种方法使数百万点之间的运输计划的计算在几分钟内。我们展示了这种方法在大脑纤维束或轨道密度图上的有效性。我们使用由此产生的平滑分配来执行基于地图集的纤维束图分割的标签转移。我们的方法的参数-模糊和到达-是有意义的，定义了两个纤维相互比较的最小和最大距离。它们可以根据解剖学知识设置。此外，我们还建议估计人口的轨道密度图作为Wasserstein重心的概率图集。我们的CUDA实现具有用户友好的PyTorch界面，可以在PyPi存储库（pip install geomloss）和www.kernel-operations.io/geomloss上免费获得。
英文摘要：
We present a new multiscale algorithm for solving regularized Optimal Transport problems on the GPU, with a linear memory footprint. Relying on Sinkhorn divergences which are convex, smooth and positive definite loss functions, this method enables the computation of transport plans between millions of points in a matter of minutes. We show the effectiveness of this approach on brain tractograms modeled either as bundles of fibers or as track density maps. We use the resulting smooth assignments to perform label transfer for atlas-based segmentation of fiber tractograms. The parameters -- blur and reach -- of our method are meaningful, defining the minimum and maximum distance at which two fibers are compared with each other. They can be set according to anatomical knowledge. Furthermore, we also propose to estimate a probabilistic atlas of a population of track density maps as a Wasserstein barycenter. Our CUDA implementation is endowed with a user-friendly PyTorch interface, freely available on the PyPi repository (pip install geomloss) and at www.kernel-operations.io/geomloss.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
